Data Engineer
Master data engineering — Python, SQL, data modeling, ETL/ELT, data warehouses, data lakes, Spark, Kafka, Airflow, streaming, cloud data services, dbt, data quality, governance, and production data pipelines. 25 phases from fundamentals to production data platforms.
Your Progress
Complete all 25 phases to master this track
Complete data engineering roadmap — 25 phases from Python and SQL foundations through data modeling, ETL/ELT, data warehouses, data lakes, Spark, Kafka, Airflow, streaming, cloud data services, dbt, data quality, governance, and production data platforms.
Phase 1: Data Engineering Foundations
Core concepts and principles of data engineering as a discipline.
What is Data Engineering
Understand the role of data engineering in modern organizations and how it enables data-driven decision making.
Data Engineer Responsibilities
Learn the daily responsibilities including pipeline development, infrastructure management, and data reliability.
DE vs Data Scientist
Compare and contrast the roles, skills, and responsibilities of data engineers and data scientists.
DE vs Analytics Engineer
Understand the distinction between data engineering and analytics engineering including tooling and focus areas.
DE vs Backend Engineer
Differentiate between data engineering and backend software engineering in terms of goals and technical requirements.
Data Lifecycle
Trace data through its entire lifecycle from creation and ingestion through processing, storage, and archival.
OLTP vs OLAP
Learn the fundamental differences between transactional and analytical processing systems and their use cases.
Structured Semi-structured and Unstructured Data
Identify and classify different data formats including tables, JSON, XML, images, and free-form text.
Batch vs Streaming
Compare batch processing and stream processing approaches including trade-offs in latency, complexity, and cost.
Data Warehouse Lake and Lakehouse
Understand the architecture and purpose of data warehouses, data lakes, and the emerging lakehouse paradigm.
ETL vs ELT
Learn the differences between Extract-Transform-Load and Extract-Load-Transform patterns and when to use each.
Phase 2: Python for Data Engineering
Python programming skills essential for building data pipelines and automation.
Python Fundamentals Review
Review core Python concepts including variables, operators, control flow, and basic syntax for data work.
Python Data Types and Structures
Master lists, dictionaries, tuples, sets, and their operations for efficient data manipulation.
Python Functions and Scope
Write reusable functions with proper parameter handling, return values, and understand variable scope rules.
Python OOP Basics
Learn classes, objects, inheritance, and encapsulation for building maintainable data pipeline components.
Python Iterators and Generators
Use iterators and generators for memory-efficient processing of large datasets without loading everything into memory.
Python Decorators
Create and use decorators to add cross-cutting concerns like logging, timing, and retry logic to functions.
Python Context Managers
Implement context managers using the with statement for resource management in file and database operations.
Python Type Hints
Apply type annotations to Python code for better documentation, IDE support, and early error detection.
Virtual Environments and pip
Create and manage Python virtual environments and install packages using pip for reproducible project setups.
Python Logging and Configuration
Configure structured logging in Python applications to track pipeline execution and debug issues effectively.
Python Testing Basics
Write unit and integration tests using pytest to validate data pipeline logic and ensure code reliability.
Python Project Structure
Organize Python projects with proper directory layout, configuration files, and packaging conventions.
Phase 3: DSA for Data Engineers
Data structures and algorithms knowledge needed for efficient data processing and pipeline design.
Arrays and Hash Maps
Use arrays and hash maps for fast data lookups, aggregations, and building efficient data processing logic.
Sets and Set Operations
Apply set data structures for deduplication, membership testing, and computing intersections and unions on datasets.
Stacks and Queues
Implement stacks for parsing and backtracking and queues for BFS traversal and task scheduling in pipelines.
Trees and Heaps
Work with binary trees for hierarchical data and heaps for priority queues in streaming top-K algorithms.
Graph Basics
Model relationships and dependencies using graphs and apply traversal algorithms for lineage and dependency resolution.
Sorting Algorithms
Understand quicksort, mergesort, and timsort implementations and when each is appropriate for data processing.
Searching Algorithms
Apply binary search and other search techniques for efficient data retrieval and range-based queries.
Complexity Analysis
Analyze time and space complexity of algorithms to choose optimal solutions for large-scale data processing.
String Processing
Master string manipulation techniques including pattern matching, tokenization, and parsing for data cleaning.
File Processing Patterns
Process large files efficiently using streaming reads, chunking, and memory-mapped approaches for data ingestion.
Memory-Efficient Algorithms
Design algorithms that minimize memory usage through streaming, sampling, and probabilistic data structures.
External Sorting Concepts
Implement external merge sort and understand disk-based sorting for datasets that exceed available memory.
Phase 4: Linux and Shell
Linux operating system skills and shell scripting for data engineering infrastructure management.
Linux Fundamentals
Navigate the Linux filesystem, use essential commands, and understand the boot process and system architecture.
Filesystems and Permissions
Manage Linux filesystems, understand file permissions, ownership, and symbolic links for secure data access.
Processes and Services
Monitor and manage Linux processes, background jobs, and systemd services that run data pipelines.
Environment Variables
Configure and manage environment variables for application settings, secrets, and pipeline configuration.
SSH and Remote Access
Establish secure remote connections to servers, transfer files, and tunnel ports for pipeline debugging.
Cron Jobs and Scheduling
Schedule recurring tasks using cron expressions and systemd timers for automated pipeline execution.
Bash Scripting
Write bash scripts for automating repetitive tasks, file transformations, and data pipeline orchestration.
Pipes and Redirection
Chain commands using pipes and redirects to build powerful data processing pipelines in the terminal.
Text Processing with grep awk sed
Use grep, awk, and sed for powerful text filtering, transformation, and extraction in log and data files.
Log Processing
Parse, filter, and analyze application and system logs to monitor pipeline health and debug failures.
Phase 5: Git and Software Engineering
Version control and software engineering best practices for collaborative data engineering.
Git Fundamentals
Initialize repositories, stage changes, commit, and understand the Git object model and working tree.
Branching and Merging
Create branches for features, merge changes, resolve conflicts, and use rebase for clean commit history.
Pull Requests
Create and manage pull requests for code review, feedback collection, and collaborative code integration.
Code Reviews
Conduct effective code reviews focusing on correctness, readability, performance, and maintainability.
Software Testing Basics
Write unit, integration, and end-to-end tests to validate data pipeline components and ensure reliability.
CI/CD Fundamentals
Set up continuous integration and deployment pipelines to automate testing and deployment of data code.
Python Project Structure
Organize Python projects with src layout, configuration files, and packaging for maintainable data code.
Packaging and Distribution
Package Python code with pyproject.toml, build wheels, and distribute internal libraries for team reuse.
Logging and Configuration
Implement structured logging and externalized configuration management for production data applications.
Documentation Best Practices
Write clear README files, docstrings, and technical documentation for data pipeline code and systems.
Phase 6: SQL Mastery
Comprehensive SQL skills for querying, transforming, and analyzing data in databases.
SELECT and WHERE
Write SELECT queries with WHERE clauses to filter and retrieve specific data from relational tables.
ORDER BY and GROUP BY
Sort query results and group data using aggregate functions to produce summary statistics.
HAVING and Aggregate Functions
Filter grouped results with HAVING and apply SUM, COUNT, AVG, MIN, MAX for data summarization.
JOINs inner outer cross
Combine rows from multiple tables using INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN, and CROSS JOIN.
Subqueries
Write nested queries for complex filtering, data comparison, and step-by-step data transformation.
Common Table Expressions
Use WITH clauses to create readable, modular SQL queries for multi-step data transformations.
Window Functions
Apply ROW_NUMBER, RANK, LAG, LEAD, and running totals for advanced analytics without collapsing rows.
CASE Expressions
Build conditional logic in SQL queries for data transformation, categorization, and pivoting results.
Set Operations
Combine query results using UNION, INTERSECT, and EXCEPT for data comparison and consolidation.
Date and Time Functions
Manipulate dates with EXTRACT, DATE_TRUNC, DATEADD, and interval arithmetic for time-based analytics.
String Functions
Transform text data using CONCAT, SUBSTRING, REPLACE, TRIM, and pattern matching with LIKE and REGEXP.
NULL Handling
Handle missing data using IS NULL, COALESCE, NULLIF, and NVL for robust data processing.
Views and Materialized Views
Create reusable query abstractions with views and performance-optimized materialized views for analytics.
Indexes and Query Optimization
Design effective indexes and rewrite queries to improve performance on large analytical datasets.
Execution Plans
Read and interpret query execution plans to identify bottlenecks, missing indexes, and optimization opportunities.
Phase 7: Database Engineering
Database architecture, optimization, and management for data engineering workloads.
PostgreSQL Architecture
Understand PostgreSQL internals including the query planner, WAL, shared buffers, and process model.
Tables and Constraints
Design tables with primary keys, foreign keys, unique, check, and not null constraints for data integrity.
Indexes B-tree and Composite
Create B-tree and composite indexes to speed up queries and understand index selection strategies.
Transactions and ACID
Implement transactions with proper isolation levels to ensure atomicity, consistency, isolation, and durability.
Multi-Version Concurrency Control
Understand how MVCC enables concurrent reads and writes without locking in modern databases.
Vacuum and Maintenance
Perform database maintenance including vacuuming, ANALYZE, reindexing, and monitoring bloat.
Query Planner
Understand cost-based query optimization, statistics, and how the planner chooses execution strategies.
Database Replication
Configure primary-replica replication for read scaling, high availability, and disaster recovery.
Table Partitioning
Split large tables by range or hash to improve query performance and simplify data lifecycle management.
MongoDB Fundamentals
Work with document-oriented storage using MongoDB for semi-structured data and flexible schemas.
DynamoDB Concepts
Design DynamoDB tables with partition keys, sort keys, GSIs, and understand provisioned vs on-demand capacity.
Key-Value and Document Stores
Compare Redis, DynamoDB, and MongoDB for different data access patterns and consistency requirements.
Phase 8: Data Modeling
Design effective data models for analytical and operational data systems.
Data Modeling Concepts
Learn the fundamentals of data modeling including entities, attributes, relationships, and cardinality.
Entities and Relationships
Identify business entities, define relationships between them, and create entity-relationship diagrams.
Normalization 1NF through 3NF
Apply normalization rules to eliminate data redundancy and ensure data integrity in relational databases.
Denormalization
Strategically denormalize data models to improve read performance for analytical queries at scale.
Star Schema
Design star schemas with central fact tables and dimension tables for efficient OLAP query performance.
Snowflake Schema
Implement normalized dimension tables in snowflake schemas to reduce storage while maintaining query structure.
Fact Tables
Design fact tables with grain, measures, and foreign keys to capture business process metrics.
Dimension Tables
Build rich dimension tables with attributes, hierarchies, and hierarchies for business context.
Slowly Changing Dimensions
Handle evolving dimension attributes using SCD Type 1, Type 2, and Type 3 strategies.
Surrogate vs Natural Keys
Choose between surrogate and natural keys for dimension tables considering performance and maintainability.
Data Marts
Create focused data marts from enterprise data warehouses for department-specific analytics needs.
Kimball Methodology
Apply the Kimball dimensional modeling approach including bus architecture and conformed dimensions.
Phase 9: ETL and ELT
Design and implement robust data extraction, transformation, and loading pipelines.
ETL Architecture
Design end-to-end ETL architectures with staging areas, transformation layers, and target loading strategies.
ELT Architecture
Implement ELT patterns that load raw data first and transform within the target warehouse using SQL.
Extract Strategies
Implement full extracts, incremental extracts, and change detection for various source systems.
Transform Patterns
Apply common transformation patterns including cleansing, deduplication, enrichment, and aggregation.
Load Strategies
Choose between bulk loading, streaming inserts, and upsert patterns for target system population.
Batch Ingestion
Build reliable batch ingestion pipelines with scheduling, monitoring, and failure recovery mechanisms.
Incremental Loads
Implement watermark-based incremental loads to process only new or changed records efficiently.
CDC and Change Data Capture
Capture and propagate database changes in real-time using log-based or trigger-based CDC techniques.
Idempotency
Design idempotent pipelines that produce the same result regardless of how many times they execute.
Data Validation
Implement validation checks at each pipeline stage to detect anomalies, missing data, and schema violations.
Error Handling and Retry
Build resilient pipelines with error handling, retry logic, dead-letter queues, and circuit breakers.
Schema Evolution and Data Lineage
Handle schema changes gracefully and track data lineage from source to destination across transformations.
Phase 10: Data Formats and Storage
Understand file formats and storage strategies for efficient data processing.
CSV and Text Files
Work with CSV and flat file formats including delimiters, encoding, escaping, and parsing challenges.
JSON and XML Formats
Parse and generate JSON and XML data with nested structures, arrays, and schema validation.
Apache Avro Format
Use Avro for compact binary serialization with embedded schemas ideal for streaming and schema evolution.
Apache Parquet Format
Read and write Parquet columnar files for efficient analytical queries with predicate pushdown.
Apache ORC Format
Leverage ORC format for optimized Hive workloads with built-in indexing and column pruning.
Compression Formats
Choose between gzip, snappy, zstd, and lz4 balancing compression ratio against read-write performance.
Serialization and Deserialization
Implement efficient serde pipelines for converting between in-memory objects and storage formats.
Columnar vs Row Storage
Compare row-oriented and columnar storage to understand when each format performs best for workloads.
Partitioning Strategies
Design table partitioning by date, region, or category to reduce scan volume and improve query speed.
Small File Problem
Solve the small file problem using compaction, bucketing, and merge strategies for optimal storage.
Phase 11: Data Warehousing
Design, build, and optimize modern data warehouses for analytical workloads.
Data Warehouse Architecture
Design multi-tier warehouse architectures with staging, integration, and presentation layers.
OLAP Concepts
Understand cubes, dimensions, measures, roll-ups, drill-downs, and slice-dice operations for analysis.
Snowflake Data Warehouse
Configure Snowflake warehouses, virtual warehouses, storage tiers, and optimize query performance.
Google BigQuery
Use BigQuery for serverless analytics with partitioned tables, clustering, and bi-engine acceleration.
Amazon Redshift
Deploy and optimize Redshift clusters with distribution keys, sort keys, and workload management.
Azure Synapse Analytics
Leverage Synapse dedicated and serverless pools for integrated analytics and data integration.
Warehouse Compute and Storage
Separate compute from storage in modern warehouses and right-size resources for cost efficiency.
Partitioning and Clustering
Apply partitioning and clustering strategies to minimize data scanned and reduce query costs.
Warehouse Query Optimization
Optimize warehouse queries using materialized results, query rewriting, and execution plan analysis.
Cost Optimization
Monitor and reduce warehouse costs through auto-scaling, suspension, and workload prioritization.
Materialized Views
Create and maintain materialized views to precompute expensive aggregations for dashboard performance.
Data Mart Design
Design focused data marts with specific grain and scope for departmental self-service analytics.
Phase 12: Data Lakes and Lakehouse
Build scalable data lakes and modern lakehouse architectures for flexible analytics.
Data Lake Architecture
Design data lake architectures with raw, processed, and curated zones for diverse analytical workloads.
Object Storage with S3
Configure S3 buckets, lifecycle policies, versioning, and cross-region replication for data lake storage.
Bronze Silver Gold Medallion
Implement the medallion architecture with bronze raw, silver cleaned, and gold business-ready layers.
Data Lakehouse Concept
Understand how lakehouses combine data lake flexibility with warehouse reliability and performance.
Delta Lake
Use Delta Lake for ACID transactions, time travel, schema enforcement, and data quality on lakes.
Apache Iceberg
Implement Apache Iceberg for open table format with hidden partitioning, schema evolution, and snapshots.
Apache Hudi
Leverage Apache Hudi for incremental processing, upserts, and near-real-time data lake pipelines.
Schema Evolution on Lakes
Handle schema changes in data lake tables including adding columns, renaming, and type changes.
Time Travel
Query historical data snapshots and audit changes using time travel capabilities in lakehouse formats.
ACID on Data Lakes
Achieve ACID transactions on object storage using Delta Lake, Iceberg, or Hudi table formats.
Data Compaction
Merge small files into optimized sizes using compaction to improve read performance and reduce costs.
Partition Management
Manage partition layouts, pruning strategies, and partition evolution for optimal lake query performance.
Phase 13: Apache Spark
Master Apache Spark for distributed data processing at scale.
Spark Architecture Driver Executors
Understand the Spark driver, executors, cluster manager, and how jobs are distributed across nodes.
Jobs Stages and Tasks
Trace how Spark breaks jobs into stages and tasks based on shuffle boundaries and narrow transformations.
Resilient Distributed Datasets
Understand RDDs as the foundation of Spark with lazy evaluation, lineage, and fault tolerance.
Spark DataFrames
Use DataFrames for structured data processing with Catalyst optimizer and Tungsten execution engine.
Spark SQL
Query data using SQL syntax in Spark, register temporary views, and leverage the Catalyst optimizer.
Transformations vs Actions
Distinguish between lazy transformations and eager actions to control when computation actually executes.
Lazy Evaluation
Understand Spark lazy evaluation, lineage graphs, and how optimization happens before execution.
Shuffles
Understand shuffle operations, their performance cost, and techniques to minimize data movement.
Partitioning Strategies
Control data partitioning with repartition, coalesce, and partition-by to optimize join and aggregation performance.
Join Strategies Broadcast Shuffle
Choose between broadcast joins, sort-merge joins, and shuffle-hash joins based on data size.
Caching and Persistence
Cache intermediate DataFrames in memory or disk to avoid recomputation in iterative algorithms.
Data Skew Handling
Detect and mitigate data skew using salting, broadcasting, and adaptive query execution.
Adaptive Query Execution
Use AQE to dynamically optimize shuffle partitions, join strategies, and skew handling at runtime.
PySpark
Build end-to-end data pipelines using PySpark with Python APIs for Spark SQL, DataFrame, and MLlib.
Phase 14: Airflow and Workflow Orchestration
Design, deploy, and manage scheduled data workflows with Apache Airflow.
DAGs and Tasks
Define directed acyclic graphs with tasks, dependencies, and execution order for data workflows.
Operators
Use BashOperator, PythonOperator, and custom operators to execute diverse tasks within Airflow DAGs.
Scheduling
Configure DAG schedules, time zones, and catchup behavior for reliable periodic pipeline execution.
Dependencies
Set up complex dependency chains with upstream, downstream, and cross-DAG triggers for orchestration.
Sensors
Wait for external conditions like file availability, API responses, or database changes before proceeding.
XCom
Pass small data between tasks using XCom for dynamic parameterization and task communication.
Variables and Connections
Manage environment-specific configuration, secrets, and connection strings through Airflow UI and backends.
Executors
Compare SequentialExecutor, LocalExecutor, and CeleryExecutor for different deployment and scaling needs.
Retries and Error Handling
Configure retry logic, on_failure callbacks, and alerting for resilient pipeline execution.
Backfills and Catchup
Run historical backfills to fill data gaps and understand catchup behavior for missed DAG runs.
Task Groups
Organize complex DAGs into logical task groups for better readability and maintainability.
DAG Testing and Deployment
Test DAGs locally with backfill and list commands, then deploy to production with proper CI/CD.
Phase 15: Streaming Fundamentals
Core concepts of real-time and near-real-time data processing systems.
Batch vs Streaming Concepts
Compare batch and streaming paradigms including latency, throughput, and exactly-once guarantees.
Event-Driven Systems
Design event-driven architectures where state changes trigger downstream processing and reactions.
Events Producers and Consumers
Model data flows with events as facts, producers as sources, and consumers as processors.
Topics and Partitions
Organize event streams into topics and partitions for parallel processing and ordered delivery.
Consumer Groups
Scale stream processing by distributing partitions across consumers in a consumer group.
Offsets and Ordering
Track consumption progress with offsets and understand ordering guarantees within partitions.
Delivery Semantics
Compare at-most-once, at-least-once, and exactly-once delivery semantics and their trade-offs.
Backpressure
Handle situations when producers generate data faster than consumers can process it in streams.
Event Replay
Replay historical events for debugging, reprocessing, and rebuilding state in streaming systems.
Stream vs Batch Tradeoffs
Evaluate when to use streaming versus batch based on latency requirements, complexity, and cost.
Phase 16: Apache Kafka
Build and operate Apache Kafka for high-throughput distributed event streaming.
Kafka Architecture
Understand Kafka brokers, topics, partitions, ZooKeeper, and the overall cluster topology.
Kafka Brokers
Configure and manage Kafka brokers including listener setup, log directories, and broker properties.
Topics and Partitions
Create topics with appropriate partition counts and replication factors for your throughput requirements.
Kafka Producers
Build Kafka producers with batching, compression, and acks configuration for reliable event publishing.
Consumers and Consumer Groups
Implement Kafka consumers with group coordination, rebalancing, and partition assignment strategies.
Offsets Management
Commit and manage consumer offsets manually or automatically to control message replay and fault tolerance.
Replication and Leader Follower
Understand ISR, leader election, and replication factors for fault tolerance and data durability.
Retention and Compaction
Configure time-based and size-based retention and log compaction for different data retention needs.
Producer Acknowledgements
Choose between acks 0, 1, and all to balance between throughput and delivery guarantee strength.
Idempotent Producers
Enable idempotent production to prevent duplicate messages from network retries and failures.
Kafka Connect
Use Kafka Connect source and sink connectors for scalable configuration-driven data integration.
Schema Registry
Manage Avro, Protobuf, or JSON schemas with Confluent Schema Registry for data governance.
Phase 17: Stream Processing
Process and analyze data in real-time using stream processing frameworks.
Stream Processing Concepts
Learn core stream processing terminology including windows, state, watermarks, and event time.
Windowing Tumbling Sliding Session
Apply tumbling, sliding, and session windows to group events for time-based aggregations.
Stateful Processing
Maintain state across events for running aggregations, joins, and pattern detection in streams.
Event Time vs Processing Time
Distinguish between when events occurred and when they are processed for accurate time-based analytics.
Watermarks
Use watermarks to track event time progress and determine when windows are complete.
Late Events Handling
Handle straggler events that arrive after window closure using allowed lateness and side outputs.
Apache Flink Concepts
Understand Flink's architecture with JobManager, TaskManagers, checkpoints, and exactly-once state.
Spark Structured Streaming
Build continuous streaming queries using Spark Structured Streaming with micro-batch and continuous modes.
Kafka Streams
Process events directly within Kafka using Kafka Streams library for lightweight stream processing.
Real-Time Analytics Project
Build an end-to-end real-time analytics dashboard from event ingestion through processing to visualization.
Phase 18: Cloud Data Engineering
Leverage AWS cloud services for building scalable data infrastructure and pipelines.
AWS IAM for Data
Configure IAM roles, policies, and permissions for secure access to AWS data services.
S3 for Data Storage
Design S3 bucket structures, lifecycle policies, and storage classes for data lake storage.
AWS Glue ETL
Build serverless ETL jobs with AWS Glue crawlers, catalogs, and auto-generated Spark scripts.
Athena Query Service
Query data in S3 using standard SQL with Amazon Athena and optimize with partitioning.
Redshift Data Warehouse
Launch and optimize Redshift clusters with node types, distribution styles, and WLM.
EMR for Spark
Run Apache Spark on Amazon EMR with instance fleets, auto-scaling, and step processing.
Lambda Serverless Functions
Write Lambda functions for event-driven data processing and API integrations.
Kinesis Streaming
Ingest and process real-time data streams with Kinesis Data Streams and Firehose.
Managed Streaming for Kafka
Deploy and manage Amazon MSK clusters for Apache Kafka without operational overhead.
RDS and DynamoDB
Use RDS for relational workloads and DynamoDB for serverless key-value storage.
CloudWatch Monitoring
Monitor data pipeline health with CloudWatch metrics, alarms, logs, and dashboards.
Step Functions
Orchestrate multi-step data workflows visually using AWS Step Functions state machines.
Data Pipeline Orchestration
Design end-to-end pipeline orchestration combining Glue, Lambda, Step Functions, and Airflow.
When to Use Each Service
Choose the right AWS service based on data volume, latency, and cost requirements.
Phase 19: Data Quality
Ensure data accuracy, completeness, and reliability across all pipeline stages.
Data Quality Dimensions
Understand the six dimensions: accuracy, completeness, consistency, timeliness, validity, and uniqueness.
Accuracy and Completeness
Validate that data values are correct and all expected records are present without gaps.
Consistency and Validity
Ensure data conforms to business rules and maintains consistency across systems and time.
Uniqueness and Freshness
Detect duplicate records and ensure data arrives within expected freshness SLAs.
Data Validation Techniques
Implement schema checks, range validation, referential integrity, and statistical validation.
Schema Validation
Validate data schemas at ingestion time to catch structural changes before they propagate.
Great Expectations Concepts
Define and validate data expectations using Great Expectations for automated quality testing.
dbt Tests
Write schema tests, data tests, and custom tests in dbt to validate warehouse transformations.
Data Contracts
Define and enforce data contracts between producers and consumers for schema and SLA compliance.
Anomaly Detection
Detect data anomalies using statistical methods, rules, and machine learning for proactive alerts.
Phase 20: dbt and Analytics Engineering
Build reliable data transformation pipelines with dbt and analytics engineering practices.
dbt Fundamentals
Set up dbt projects, configure profiles, and understand the core workflow of model-test-document.
Models and Sources
Define source freshness, staging models, and intermediate models for a well-structured dbt project.
Seeds
Load CSV seed files into dbt for reference data, mapping tables, and static configuration data.
Tests
Write unique, not-null, accepted-values, and custom generic tests to validate data transformations.
Macros and Jinja
Create reusable SQL macros with Jinja templating for DRY transformation logic across models.
Incremental Models
Build incremental models that process only new or changed data for efficient large-table transformations.
Snapshots
Track historical changes in source data using dbt snapshots with SCD Type 2 logic.
Documentation and Lineage
Generate documentation sites and visual lineage graphs to understand model dependencies.
CI with dbt
Set up continuous integration to test and preview dbt model changes before merging to production.
Semantic Layer Concepts
Define metrics and dimensions in a semantic layer for consistent business metric definitions.
Data Contracts with dbt
Enforce data contracts between upstream producers and downstream consumers using dbt tests and packages.
Analytics Engineering Patterns
Apply patterns like staging-mart, activity schema, and wide tables for scalable analytics.
Phase 21: Data Governance and Security
Implement data governance frameworks and security controls for enterprise data systems.
Data Governance Framework
Establish organizational data governance with policies, standards, roles, and accountability structures.
Data Ownership
Define data ownership roles, responsibilities, and accountability for data quality and access decisions.
Data Catalog
Implement a data catalog to document datasets, schemas, owners, and usage patterns across the organization.
Data Lineage
Track data lineage from source systems through transformations to final analytical outputs.
Metadata Management
Collect, store, and use technical and business metadata to improve data discoverability and trust.
PII Handling
Identify, classify, and protect personally identifiable information in data pipelines and storage.
Encryption at Rest and in Transit
Implement encryption for data at rest in storage and in transit across network connections.
IAM and Access Control
Design role-based access control policies to grant least-privilege access to data resources.
Row and Column Level Security
Implement row-level and column-level security to restrict data access based on user roles.
Masking and Tokenization
Protect sensitive data using masking, tokenization, and anonymization techniques for safe analysis.
Audit Logging
Record and monitor all data access and modification events for compliance and security auditing.
Retention Policies
Define and enforce data retention and deletion policies aligned with legal and regulatory requirements.
Phase 22: Data Observability and Reliability
Monitor, alert, and maintain reliability of data pipelines in production.
Pipeline Monitoring
Set up comprehensive monitoring for pipeline health including run times, row counts, and error rates.
Pipeline Failures
Diagnose and resolve common pipeline failures including data format changes and resource exhaustion.
Data Freshness Monitoring
Track and alert on data freshness to ensure downstream consumers receive timely updates.
Data Quality Monitoring
Continuously monitor data quality metrics and alert on degradation of accuracy or completeness.
Lineage Tracking
Track data flow across systems to understand impact of changes and debug downstream issues.
SLAs and SLOs
Define service level agreements and objectives for pipeline freshness, completeness, and accuracy.
Alerting Strategies
Design effective alerting with proper thresholds, escalation paths, and noise reduction.
Incident Management
Establish incident response processes for data issues including triage, resolution, and postmortems.
Retry Strategies
Implement exponential backoff, circuit breakers, and dead-letter queues for transient failures.
Idempotent Pipelines
Design pipelines that produce consistent results regardless of repeated execution or failure recovery.
Phase 23: Data Engineering Architecture
Design scalable, reliable, and cost-effective data architecture patterns.
Batch Architecture Patterns
Design batch processing architectures with scheduling, staging, transformation, and loading layers.
Streaming Architecture Patterns
Implement streaming architectures with event ingestion, processing, and real-time storage layers.
Lambda Architecture
Understand the Lambda architecture combining batch and speed layers for comprehensive data processing.
Kappa Architecture
Implement the Kappa architecture using only streaming for both real-time and historical data processing.
Data Lake Architecture Patterns
Design lake architectures with medallion layers, zone separation, and governance controls.
Data Warehouse Architecture Patterns
Design warehouse topologies including single-tenant, multi-tenant, and federated architectures.
Lakehouse Architecture
Combine data lake flexibility with warehouse reliability using modern lakehouse architectural patterns.
CDC Architecture Patterns
Design change data capture architectures for real-time database replication and event streaming.
Event-Driven Architecture
Build event-driven systems with event sourcing, CQRS, and asynchronous message-based communication.
Data Partitioning Strategies
Design partitioning schemes across storage systems to optimize query performance and data management.
Scalability Patterns
Apply horizontal scaling, sharding, and read-replica patterns for growing data volumes and user loads.
Cost Optimization Architecture
Design architectures that minimize cloud costs through right-sizing, auto-scaling, and workload separation.
Phase 24: Production Data Engineering Projects
Apply skills through hands-on production-grade data engineering projects.
Batch ETL Pipeline Project
Build a complete batch ETL pipeline extracting from APIs, transforming with Python, and loading to a warehouse.
Cloud Data Warehouse Project
Deploy a cloud data warehouse on Redshift or Snowflake with staging, transformations, and BI connectivity.
Real-Time Streaming Pipeline Project
Build a real-time streaming pipeline with Kafka, process events, and store results for live dashboards.
Data Lakehouse Project
Implement a lakehouse architecture using Delta Lake or Iceberg with Bronze-Silver-Gold layers on S3.
Production Data Platform Project
Design and deploy a complete data platform with ingestion, processing, storage, and serving layers.
E-Commerce Data Platform Capstone
Build a comprehensive e-commerce data platform handling orders, inventory, customer analytics, and reporting.
Phase 25: Data Engineer Interview Preparation
Prepare for data engineer interviews with targeted practice across all core topics.
SQL Interview Questions
Practice complex SQL problems including window functions, CTEs, joins, and optimization scenarios.
Python Interview Questions
Solve Python coding challenges covering data structures, file processing, and pipeline logic.
Data Modeling Interview
Design data models for business scenarios including dimensional modeling and schema design.
ETL and ELT Interview
Discuss ETL architecture decisions, error handling strategies, and incremental loading approaches.
Spark Interview Questions
Solve Spark coding challenges including DataFrame operations, joins, and performance optimization.
Kafka Interview Questions
Answer Kafka architecture questions on partitions, consumer groups, exactly-once, and Connect.
Airflow Interview Questions
Discuss DAG design, scheduling, retries, and production Airflow deployment best practices.
AWS Data Service Interview
Explain when to use Glue vs EMR, Athena vs Redshift, and design AWS data architectures.
Data Warehouse Interview
Discuss warehouse design patterns, partitioning strategies, and query optimization approaches.
Data Lake Interview
Explain lake vs lakehouse trade-offs, Iceberg vs Delta Lake, and medallion architecture design.
System Design Interview
Design end-to-end data systems from requirements gathering through architecture to implementation.
Pipeline Design Interview
Design data pipelines for specific business requirements including SLAs, scaling, and fault tolerance.
Debugging Scenarios
Practice diagnosing and resolving common data pipeline issues from symptoms to root causes.
Performance Optimization
Optimize slow queries, memory-hungry jobs, and bottlenecked pipelines for production workloads.
Project Deep Dives
Prepare detailed narratives about past data projects including architecture decisions and trade-offs.